Machine learning applications to sports event prediction and detection of match-fixing in sports betting markets

机器学习成功地用于许多分类和预测目的，但最先进的体育预测不愿意正确地接受现代机器学习方法，如深度神经网络。相反，统计模型通常被用于体育预测，并且经常被发现是上级的。例如，Ingram [1]为网球提出的基于点的贝叶斯分层模型，以及狄克逊和科尔斯[2]提出的足球泊松回归模型。尽管如此，最近的一些进展证明了机器学习如何在体育预测中取得成功。Wilkens [3]在2021年的一篇综述中总结了网球比赛中的机器学习预测方法，并认为需要进一步研究，因为这些策略中的许多策略仍然存在缺陷和不准确，这一点在博彩应用中表现得淋漓尽致。

Machine learning is successfully used for many classification and prediction purposes, but state-of-the-art sports prediction is reluctant to properly embrace modern machine learning methods such as deep neural networks. Instead, statistical models are generally favoured for sports prediction, and have often been found to be superior. For example, the point-based Bayesian hierarchical model proposed by Ingram [1] for tennis, and the seminal paper by Dixon and Coles [2] that proposed a Poisson regression model for football. Despite this, there are recent advancements that demonstrate how machine learning can be successful in sports prediction. A 2021 review by Wilkens [3] summarizes the machine learning prediction methods in the case of tennis and argues for further research since many of these strategies are still flawed and inaccurate, illustrated by their unprofitability in betting applications.